Data traffic in a communication network can be described as statistically self-similar. Observed characteristics of such data traffic include burstiness on multiple time scales, highly variable traffic, and as having heavy-tailed distributions of file sizes and corresponding transmission times. Observed data traffic patterns differ from observed voice traffic patterns, and commonly used voice traffic models typically do not accurately describe data traffic behavior.
Network performance degrades gradually with increasing self-similarity of data traffic. For example, the more self-similar the traffic, the longer the queue length. Under such circumstances heavy fluctuations in packet delay can arise, which can cause, among other things, jitter in data communication, which typically leads to deterioration of application performance. Determining the burstiness of a communication system is thus highly desirable for operators and managers of such systems. Common methodologies for measuring network utilization involved periodic sample measurements over time include, for example, measuring the average throughput at a network element, or between network elements, over a period time. Such methodologies cannot measure the burstiness of the samples within the analyzed time interval.
Overview
Active wireless devices in communication with an access node of a wireless communication system are detected, and the active wireless devices are prioritized according to prioritization criteria. A group of the active wireless devices is selected from among the plurality of active wireless devices. Data is provided to each selected wireless device in at least one modulation and coding scheme, and a burstiness metric is received based on the provided data. Based on the received burstiness metrics, a burstiness profile of the wireless communication system is determined.